Presentation + Paper
16 March 2020 Automatic brain structure-guided registration of pre and intra-operative 3D ultrasound for neurosurgery
Soumya Ghose, David Mills, Jhimli Mitra, L. Scott Smith, Desmond Teck-Beng Yeo, Alexandra Golby, Sarah Frisken, Thomas K. Foo
Author Affiliations +
Abstract
Image guidance aids neurosurgeons in making critical clinical decisions of safe maximal resection of diseased tissue. The brain however undergoes significant non-linear structural deformation on account of dura opening and tumor resection. Deformable registration of pre-operative ultrasound to intra-operative ultrasound may be used in mapping of pre-operative planning MRI to intraoperative ultrasound. Such mapping may aid in determining tumor resection margins during surgery. In this work, brain structures visible in pre- and intra-operative 3D ultrasound were used for automatic deformable registration. A Gaussian mixture model was used to automatically segment structures of interest in pre- and intra-operative ultrasound and patch-based normalized cross-correlation was used to establish correspondences between segmented structures. An affine registration based on correspondences was followed by B-spline based deformable registration to register pre- and intra-operative ultrasound. Manually labelled landmarks in pre- and intra-operative ultrasound were used to quantify the mean target registration error. We achieve a mean target registration error of 1.43±0.8 mm when validated with 17 pre- and intra-operative ultrasound image volumes of a public dataset.
Conference Presentation
© (2020) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Soumya Ghose, David Mills, Jhimli Mitra, L. Scott Smith, Desmond Teck-Beng Yeo, Alexandra Golby, Sarah Frisken, and Thomas K. Foo "Automatic brain structure-guided registration of pre and intra-operative 3D ultrasound for neurosurgery", Proc. SPIE 11315, Medical Imaging 2020: Image-Guided Procedures, Robotic Interventions, and Modeling, 113150J (16 March 2020); https://doi.org/10.1117/12.2549630
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KEYWORDS
Ultrasonography

Image registration

Brain

Tumors

Image segmentation

Magnetic resonance imaging

Surgery

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